About the Data

Conditions

Conventional fish treated with loperamide at 5 dpf for 24 hours.

T0. Sample timepoint 1 at 6 dpf (24 hour treatment)
T1. Sample timepoint 2 at 7 dpf (24 hour treatment + 24 hour water)
T5. Sample timepoint 3 at 11 dpf (24 hour treatment + 5 days water)

ID	SeqName	SampleName	Project	FishType	LoperamidePathogen	SampleRep	Timepoint	TimepointDay
1	1_Conv_1_1	Conv_1_1	Loperamide	Conv		1	1	24h
2	2_Conv_1_2	Conv_1_2	Loperamide	Conv		2	1	24h
3	3_Conv_1_3	Conv_1_3	Loperamide	Conv		3	1	24h
4	4_Conv_1_4	Conv_1_4	Loperamide	Conv		4	1	24h
5	5_Conv_1_5	Conv_1_5	Loperamide	Conv		5	1	24h
6	6_DMSO_1_1	DMSO_1_1	Loperamide	Conv	DMSO 1:100	1	1	24h
7	7_DMSO_1_2	DMSO_1_2	Loperamide	Conv	DMSO 1:100	2	1	24h
8	8_DMSO_1_3	DMSO_1_3	Loperamide	Conv	DMSO 1:100	3	1	24h
9	9_DMSO_1_4	DMSO_1_4	Loperamide	Conv	DMSO 1:100	4	1	24h
10	10_DMSO_1_5	DMSO_1_5	Loperamide	Conv	DMSO 1:100	5	1	24h
11	11_LOP_1_1	LOP_1_1	Loperamide	Conv	Loperamide 10mg/L	1	1	24h
12	12_LOP_1_2	LOP_1_2	Loperamide	Conv	Loperamide 10mg/L	2	1	24h
13	13_LOP_1_3	LOP_1_3	Loperamide	Conv	Loperamide 10mg/L	3	1	24h
14	14_LOP_1_4	LOP_1_4	Loperamide	Conv	Loperamide 10mg/L	4	1	24h
15	15_LOP_1_5	LOP_1_5	Loperamide	Conv	Loperamide 10mg/L	5	1	24h
16	16_Conv_2_1	Conv_2_1	Loperamide	Conv		1	2	48h
17	17_Conv_2_2	Conv_2_2	Loperamide	Conv		2	2	48h
18	18_Conv_2_3	Conv_2_3	Loperamide	Conv		3	2	48h
19	19_Conv_2_4	Conv_2_4	Loperamide	Conv		4	2	48h
20	20_Conv_2_5	Conv_2_5	Loperamide	Conv		5	2	48h
21	21_DMSO_2_1	DMSO_2_1	Loperamide	Conv	DMSO 1:100	1	2	48h
22	22_DMSO_2_2	DMSO_2_2	Loperamide	Conv	DMSO 1:100	2	2	48h
23	23_DMSO_2_3	DMSO_2_3	Loperamide	Conv	DMSO 1:100	3	2	48h
24	24_DMSO_2_4	DMSO_2_4	Loperamide	Conv	DMSO 1:100	4	2	48h
25	25_DMSO_2_5	DMSO_2_5	Loperamide	Conv	DMSO 1:100	5	2	48h
26	26_LOP_2_1	LOP_2_1	Loperamide	Conv	Loperamide 10mg/L	1	2	48h
27	27_LOP_2_2	LOP_2_2	Loperamide	Conv	Loperamide 10mg/L	2	2	48h
28	28_LOP_2_3	LOP_2_3	Loperamide	Conv	Loperamide 10mg/L	3	2	48h
29	29_LOP_2_4	LOP_2_4	Loperamide	Conv	Loperamide 10mg/L	4	2	48h
30	30_LOP_2_5	LOP_2_5	Loperamide	Conv	Loperamide 10mg/L	5	2	48h
31	31_Conv_3_1	Conv_3_1	Loperamide	Conv		1	3	6d
32	32_Conv_3_2	Conv_3_2	Loperamide	Conv		2	3	6d
33	33_Conv_3_3	Conv_3_3	Loperamide	Conv		3	3	6d
34	34_Conv_3_4	Conv_3_4	Loperamide	Conv		4	3	6d
35	35_Conv_3_5	Conv_3_5	Loperamide	Conv		5	3	6d
36	36_DMSO_3_1	DMSO_3_1	Loperamide	Conv	DMSO 1:100	1	3	6d
37	37_DMSO_3_2	DMSO_3_2	Loperamide	Conv	DMSO 1:100	2	3	6d
38	38_DMSO_3_3	DMSO_3_3	Loperamide	Conv	DMSO 1:100	3	3	6d
39	39_DMSO_3_4	DMSO_3_4	Loperamide	Conv	DMSO 1:100	4	3	6d
40	40_DMSO_3_5	DMSO_3_5	Loperamide	Conv	DMSO 1:100	5	3	6d
41	41_LOP_3_1	LOP_3_1	Loperamide	Conv	Loperamide 10mg/L	1	3	6d
42	42_LOP_3_2	LOP_3_2	Loperamide	Conv	Loperamide 10mg/L	2	3	6d
43	43_LOP_3_3	LOP_3_3	Loperamide	Conv	Loperamide 10mg/L	3	3	6d
44	44_LOP_3_4	LOP_3_4	Loperamide	Conv	Loperamide 10mg/L	4	3	6d
45	45_LOP_3_5	LOP_3_5	Loperamide	Conv	Loperamide 10mg/L	5	3	6d

Sampling

Transfer fish into a small petri dish full of sterile water (wash #1).
Wash the fish again by transferring into a new petri dish full of sterile water (wash #2).
Add 5 washed fish per tube into 5 x 2 mL tubes, with ~500 µL of water
After all samples have been collected for all conditions, remove as much water as possible with a p200 pipette
Put the samples at -80C

Sequencing

16S V6 region

Primers

1064R GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGCGACRRCCATGCANCACCT
967F-A TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCTAACCGANGAACCTYACC
967F-B TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCNACGCGAAGAACCTTANC
967F-C TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCAACGCGMARAACCTTACC
967F-D TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGATACGCGARGAACCTTACC

2 x 250 bp paired-end Illumina sequencing

Analysis

Initially done with QIIME2, dada2 denoising. Eukaryotic reads removed here.

Setup

Load libraries and settings

library(tidyverse)
library(scales)
library(ggpubr)
library(qiime2R)
library(vegan)
library(gt)
library(microbiomeMarker)
library(patchwork)
library(ggh4x)
library(ggtext)

# set global theme
theme_set(theme_minimal()+
             theme(panel.grid.major.y = element_line(color="grey80"), strip.text=element_text(size=16),
                   strip.background = element_rect(color="transparent"),
                   strip.text.y = element_text(angle=0), plot.caption = element_text(size=10),
                   panel.grid.major.x = element_blank(),legend.position="top",
                   plot.background = element_rect(fill="transparent", color="transparent"),
                   axis.ticks = element_line(inherit.blank = FALSE),
                   panel.background = element_rect(color="grey50", size=2), 
                   legend.title = element_text(size=18),
                   axis.text = element_text(size=15), axis.title = element_text(size=18),
                   legend.text = element_text(size=16), plot.title = element_text(hjust=0.5)))

# set global options for code output
knitr::opts_chunk$set(warning=FALSE,message=FALSE)

Import data

metadatafile <- "../metadata/YersiniaLoperamide16Samplicon_metadata.txt"
tablefile <- "../output/table.qza"
taxonomyfile <- "../output/taxonomy.qza"
treefile <- "../output/rooted-tree.qza"

SVs<-read_qza(tablefile)$data
head(SVs)[,1:5]

##                                  1-Conv-1-1 10-DMSO-1-5 100-Conv-Fcol-5
## cca00a58137be2c40384b70e46bda58e       2835        2386           70339
## 4de03c7aceccf0fcb66b3589366454da       1005        3057           12568
## 82fc1666a2536a3cd12196a8034595ef        363         514            1753
## 9573a7fa5cbd06bf5889768443cc18e4        581        9145           14308
## aa232c50f749cc656fab586f02d3b071         27           0              10
## 8accec78e96e26062eb54ddedc86f5a2        901        2545            5487
##                                  101-Mock-1 102-Mock-2
## cca00a58137be2c40384b70e46bda58e        197         43
## 4de03c7aceccf0fcb66b3589366454da         66         21
## 82fc1666a2536a3cd12196a8034595ef         18         16
## 9573a7fa5cbd06bf5889768443cc18e4         59         23
## aa232c50f749cc656fab586f02d3b071          0          6
## 8accec78e96e26062eb54ddedc86f5a2         19          0

metadata<-read_q2metadata(metadatafile)  %>% 
   mutate(Loperamide=case_when(LoperamidePathogen == "DMSO 1:100" ~ "DMSO",
                                       LoperamidePathogen == "Loperamide 10mg/L" ~ "Loperamide",
                                       TRUE ~ "Control water"))
head(metadata)[,1:5]

##     SampleID SampleName    Project FishType LoperamidePathogen
## 1 1-Conv-1-1   Conv_1_1 Loperamide     Conv                   
## 2 2-Conv-1-2   Conv_1_2 Loperamide     Conv                   
## 3 3-Conv-1-3   Conv_1_3 Loperamide     Conv                   
## 4 4-Conv-1-4   Conv_1_4 Loperamide     Conv                   
## 5 5-Conv-1-5   Conv_1_5 Loperamide     Conv                   
## 6 6-DMSO-1-1   DMSO_1_1 Loperamide     Conv         DMSO 1:100

taxonomy<-read_qza(taxonomyfile)$data %>% parse_taxonomy()
head(taxonomy)[,1:5]

##                                       Kingdom         Phylum
## cca00a58137be2c40384b70e46bda58e  d__Bacteria Proteobacteria
## 4de03c7aceccf0fcb66b3589366454da  d__Bacteria Proteobacteria
## 82fc1666a2536a3cd12196a8034595ef  d__Bacteria Proteobacteria
## 9573a7fa5cbd06bf5889768443cc18e4 d__Eukaryota     Vertebrata
## aa232c50f749cc656fab586f02d3b071  d__Bacteria Proteobacteria
## 8accec78e96e26062eb54ddedc86f5a2 d__Eukaryota     Vertebrata
##                                                Class            Order
## cca00a58137be2c40384b70e46bda58e Gammaproteobacteria    Aeromonadales
## 4de03c7aceccf0fcb66b3589366454da Gammaproteobacteria             <NA>
## 82fc1666a2536a3cd12196a8034595ef Alphaproteobacteria      Rhizobiales
## 9573a7fa5cbd06bf5889768443cc18e4      Actinopterygii        Teleostei
## aa232c50f749cc656fab586f02d3b071 Gammaproteobacteria Enterobacterales
## 8accec78e96e26062eb54ddedc86f5a2      Actinopterygii        Teleostei
##                                          Family
## cca00a58137be2c40384b70e46bda58e Aeromonadaceae
## 4de03c7aceccf0fcb66b3589366454da           <NA>
## 82fc1666a2536a3cd12196a8034595ef   Rhizobiaceae
## 9573a7fa5cbd06bf5889768443cc18e4      Teleostei
## aa232c50f749cc656fab586f02d3b071           <NA>
## 8accec78e96e26062eb54ddedc86f5a2      Teleostei

physeq<-qza_to_phyloseq(
    features=tablefile,
    tree=treefile,
    taxonomy=taxonomyfile,
    metadata = metadatafile
    )

Clean up data

metadata$LoperamideTimepoint <- factor(metadata$LoperamideTimepoint, 
                          levels = c("ControlWater.24h","ControlWater.48h","ControlWater.6d",
                                     "DMSO.24h", "DMSO.48h","DMSO.6d",
                                     "Loperamide.24h","Loperamide.48h","Loperamide.6d"),
                          labels = c("ControlWater.T0","ControlWater.T1","ControlWater.T5",
                                     "DMSO.T0", "DMSO.T1","DMSO.T5",
                                     "Loperamide.T0","Loperamide.T1","Loperamide.T5"))

metadata$TimepointDay <- factor(metadata$TimepointDay, 
                          levels = c("24h","48h","6d"),
                          labels = c("T0","T1","T5"))


# select just Loperamide project data
metadataLoperamide <- filter(metadata, Project=="Loperamide" | Project=="Controls")
taxasums <- qiime2R::summarize_taxa(SVs, taxonomy) 

# vector of eukaryotic & cyanobacteria ASVs
eukASVs <- taxonomy %>% filter(Kingdom=="d__Eukaryota" | Kingdom=="d__Archaea" | Phylum=="Cyanobacteria") %>% 
   rownames_to_column("ASVs") %>% .$ASVs

# just taxa data in loperamide study, without archea/chloroplasts/eukaryotic
taxasumsclean <- select(as.data.frame(SVs), metadataLoperamide$SampleID) %>% 
   rownames_to_column("ASV") %>% 
   filter(!ASV %in% eukASVs) %>% 
   column_to_rownames(var="ASV") %>% qiime2R::summarize_taxa(taxonomy)

Make data frames per rank

datafullphylum <- 
   # transpose the phylum table of taxa sums
   t(taxasumsclean$Phylum) %>% 
   # make into data frame
   as.data.frame() %>% 
   # put rownames as SampleID
   rownames_to_column("SampleID") %>% 
   # join with the metadata
   full_join(metadata) %>% 
   # convert to long format
   pivot_longer("d__Bacteria; Abditibacteriota":"Unassigned; NA")

datafullkingdom <- 
   t(taxasumsclean$Kingdom) %>%  as.data.frame() %>% rownames_to_column("SampleID") %>% 
   full_join(metadata) %>% pivot_longer("d__Bacteria":"Unassigned")

datafullclass <- 
   t(taxasumsclean$Class) %>% as.data.frame() %>% rownames_to_column("SampleID") %>% 
   full_join(metadata) %>% 
   pivot_longer("d__Bacteria; Abditibacteriota; Abditibacteria":"Unassigned; NA; NA")

datafullorder <- 
   t(taxasumsclean$Order) %>% as.data.frame() %>% rownames_to_column("SampleID") %>% 
   full_join(metadata) %>% 
   pivot_longer("d__Bacteria; Abditibacteriota; Abditibacteria; Abditibacteriales":"Unassigned; NA; NA; NA")

datafullfamily <- 
   t(taxasumsclean$Family) %>% as.data.frame() %>% rownames_to_column("SampleID") %>% 
   full_join(metadata) %>% 
   pivot_longer("d__Bacteria; Abditibacteriota; Abditibacteria; Abditibacteriales; Abditibacteriaceae":
                   "Unassigned; NA; NA; NA; NA")

datafullgenus <- 
   t(taxasumsclean$Genus) %>% as.data.frame() %>% rownames_to_column("SampleID") %>% 
   full_join(metadata) %>% 
   pivot_longer("d__Bacteria; Abditibacteriota; Abditibacteria; Abditibacteriales; Abditibacteriaceae; Abditibacterium":
                   "Unassigned; NA; NA; NA; NA; NA")

datafullASVs <- t(SVs) %>% as.data.frame() %>% rownames_to_column("SampleID") %>% 
   full_join(metadata) %>% 
   pivot_longer("cca00a58137be2c40384b70e46bda58e":"213dc85b14c8cdc3e765c92bcc6fa6a2", names_to = "ASV") %>% 
   filter(Project=="Loperamide" | Project=="Controls") %>% 
   filter(!ASV %in% eukASVs)

Other formats

# matrix format
metadataLoperamide <- filter(metadata, Project=="Loperamide")
matrixLoperamide <- select(as.data.frame(SVs), metadataLoperamide$SampleID) %>% 
   rownames_to_column("ASV") %>% 
   filter(!ASV %in% eukASVs) %>% 
   column_to_rownames(var="ASV") %>% t()

matrixLoperamidePercent <- select(as.data.frame(SVs), metadataLoperamide$SampleID) %>% 
   rownames_to_column("ASV") %>% 
   filter(!ASV %in% eukASVs) %>% 
   column_to_rownames(var="ASV") %>% 
   mutate_if(is.numeric, proportions) %>% t()

# physeq format
Loperamidephyseq <- phyloseq::subset_samples(physeq, Project=="Loperamide") %>% 
   phyloseq::subset_taxa((Kingdom!="d__Eukaryota") | is.na(Kingdom)) %>% 
   phyloseq::subset_taxa((Kingdom!="d__Archaea") | is.na(Kingdom)) %>% 
   phyloseq::subset_taxa((Phylum!="Cyanobacteria") | is.na(Phylum))

length(Loperamidephyseq@tax_table)

## [1] 91770

General QC

Reads per sample

# define some distinct palettes
palette<-c("grey50","#D95F02", "#1B9E77", "#E6AB02","#7570B3")
palettep<-c("skyblue","#D95F02", "#1B9E77", "#E6AB02","#7570B3", "#66A61E", "gray30")
palettess<-c("#A6CEE3", "#1F78B4", "#B2DF8A", "#33A02C", "#FB9A99", "#E31A1C", "#FDBF6F", "#FF7F00", "#CAB2D6", "#6A3D9A", "#FFFF99", "#B15928","gray58","grey40")

# total QCd reads
datafullkingdom %>% filter(Project=="Loperamide") %>% summarise(NumReads = sum(value))

## # A tibble: 1 × 1
##   NumReads
##      <dbl>
## 1  2161882

# plot reads per sample
readspersampleplot <- datafullkingdom %>% filter(Project=="Loperamide") %>% 
   ggplot(aes(x=factor(SampleName,levels=unique(metadata$SampleName)), 
              y=value, fill=Loperamide))+
   facet_nested(.~TimepointDay+Loperamide, scales="free", space="free")+
   geom_col()+
   theme(legend.position = "none", axis.text.x = element_text(size=10, angle=40, hjust=1), 
         strip.background = element_rect(color="white", fill="grey85"),
         axis.ticks.y = element_line(inherit.blank=FALSE, color="grey30"))+
   scale_fill_manual(values=c('#000000', '#1c5580', '#0fc08e'))+
   scale_y_continuous(expand=c(0,0), labels=scales::label_comma())+
   labs(y="Number of reads per sample",x=NULL,fill=NULL)
readspersampleplot

Rarefaction curves

(S <- specnumber(matrixLoperamide))

##  1-Conv-1-1  2-Conv-1-2  3-Conv-1-3  4-Conv-1-4  5-Conv-1-5  6-DMSO-1-1 
##         864         707         560         654         691         222 
##  7-DMSO-1-2  8-DMSO-1-3  9-DMSO-1-4 10-DMSO-1-5  11-LOP-1-1  12-LOP-1-2 
##         713         392         455         354         315         376 
##  13-LOP-1-3  14-LOP-1-4  15-LOP-1-5 16-Conv-2-1 17-Conv-2-2 18-Conv-2-3 
##         165         341         105         526         557         336 
## 19-Conv-2-4 20-Conv-2-5 21-DMSO-2-1 22-DMSO-2-2 23-DMSO-2-3 24-DMSO-2-4 
##         414         273         391         236         231         474 
## 25-DMSO-2-5  26-LOP-2-1  27-LOP-2-2  28-LOP-2-3  29-LOP-2-4  30-LOP-2-5 
##         816         621         456         526         578         701 
## 31-Conv-3-1 32-Conv-3-2 33-Conv-3-3 34-Conv-3-4 35-Conv-3-5 36-DMSO-3-1 
##         503         618         400         987         657        1173 
## 37-DMSO-3-2 38-DMSO-3-3 39-DMSO-3-4 40-DMSO-3-5  41-LOP-3-1  42-LOP-3-2 
##         575         938         848         631         865         600 
##  43-LOP-3-3  44-LOP-3-4  45-LOP-3-5 
##         612         648         672

(raremax <- min(rowSums(matrixLoperamide)))

## [1] 4184

Srare <- vegan::rarefy(matrixLoperamide, raremax)
Sslope <- vegan::rareslope(matrixLoperamide, raremax)


plot(S, Srare, xlab = "Observed No. of ASVs", ylab = "Rarefied No. of ASVs")
abline(0,1)

plot(S, Sslope, xlab = "Observed No. of ASVs", ylab = "Slope at rarefied sample size")

rarecurve_data <- rarecurve(matrixLoperamide, step = 100, sample = raremax)

map_dfr(rarecurve_data, bind_rows) %>% 
   bind_cols(SampleID = rownames(matrixLoperamide),.) %>%
   pivot_longer(-SampleID) %>%
   drop_na() %>%
   mutate(n_seqs = as.numeric(str_replace(name, "N", ""))) %>%
   select(-name) %>%
   left_join(metadata) %>% 
   ggplot(aes(x=n_seqs, y=value, group=SampleID, color=LoperamideTimepoint, lty=Loperamide)) +
   geom_line(lwd=0.8) +
   scale_color_manual(values=c("grey80","grey50","grey20","#a7a7ff","#0000ce", 
                              "#00006c","aquamarine1", "aquamarine3","aquamarine4"))+
   scale_linetype_manual(values=c("solid","dotted","dashed"))+
   scale_x_continuous(labels=label_comma())+
   theme(legend.key.width = unit(1.33,"cm"), legend.position="right",
         strip.background = element_rect(color="white", fill="grey85"))+
   labs(x = "Number of sequences", y="Number of ASVs detected", color=NULL, lty=NULL)

rareplot <- map_dfr(rarecurve_data, bind_rows) %>% 
   bind_cols(SampleID = rownames(matrixLoperamide),.) %>%
   pivot_longer(-SampleID) %>%
   drop_na() %>%
   mutate(n_seqs = as.numeric(str_replace(name, "N", ""))) %>%
   select(-name) %>%
   left_join(metadata) %>% 
   ggplot(aes(x=n_seqs, y=value, group=SampleID, color=Loperamide, lty=Loperamide)) +
   geom_line(lwd=0.8) +
   facet_grid(.~TimepointDay)+
   scale_color_manual(values=c('#000000', '#1c5580', '#0fc08e'))+
   scale_linetype_manual(values=c("solid","dotted","dashed"))+
   scale_x_continuous(labels=label_comma())+
   theme(legend.key.width = unit(1.33,"cm"), legend.position=c(0.9,0.18),
         strip.background = element_rect(color="white", fill="grey85"))+
   labs(x = "Number of sequences", y="Number of ASVs detected", color=NULL, lty=NULL)

readspersampleplot / rareplot + plot_annotation(tag_levels = "A") &
   theme(plot.tag = element_text(face = "bold", size=20))

ggsave("../figures/FigureS1_16SLoperamideQC_rarecurve_reads.png", width=14, height=10)
ggsave("../figures/FigureS1_16SLoperamideQC_rarecurve_reads.pdf", width=14, height=10)
ggsave("../figures/FigureS1_16SLoperamideQC_rarecurve_reads.tiff", width=14, height=10)

Controls

Expected mock community

expectedmock <- data_frame(
   SampleName="Expected_Mock",
   value=0,
   MockTaxa=factor(c("Pseudomonas aeruginosa", "Escherichia coli", 
                     "Salmonella enterica", "Enterococcus faecalis", 
                     "Lactobacillus fermentum","Staphylococcus aureus", 
                     "Listeria monocytogenes", "Bacillus subtilis")),
   percent=c(4.2,10.1,10.4,9.9,
            18.4,15.5,14.1,17.4))


mockplot<- expectedmock %>% 
   ggplot(aes(x=SampleName, y=percent, fill=MockTaxa))+
   geom_col(position="fill", alpha=0.8)+
   theme(legend.text = element_text(size=12, colour="gray20", margin = margin(b = 10, unit = "pt")),
         legend.position = "right",legend.direction = "vertical",
         axis.ticks.y = element_line(inherit.blank=FALSE, color="grey30"))+
   scale_fill_manual(values=c("#c5543a",
"#b8995a",
"#9cd250",
"#6eb386",
"#8c9fc3",
"#7e4abb",
"#c45891",
"#4b393e"))+
   scale_y_continuous(labels = scales::percent_format(), expand=c(0,0))+
   labs(y="Expected mock species abundance",x=NULL,fill="Mock species")+ 
   guides(fill = guide_legend(ncol = 2))
mockplot

conreads<- datafullkingdom %>% filter(Project=="Controls") %>% 
   ggplot(aes(x=SampleName, y=value))+
   geom_col()+
   theme(legend.text = element_text(size=12, colour="gray20"),
         axis.ticks.y = element_line(inherit.blank=FALSE, color="grey30"))+
   scale_y_continuous(expand=c(0,0), labels=scales::label_comma())+
   labs(y="Number of reads per sample",x=NULL,fill=NULL)

palettess2<-c("#A6CEE3", "#1F78B4", "#B2DF8A", "#33A02C", "#FB9A99", "#E31A1C", "#FDBF6F", "#FF7F00", 
              "#CAB2D6", "#6A3D9A", "#FFFF99", "#B15928","blue1","navy",
              "maroon2","maroon4","burlywood1","burlywood4","aquamarine3","aquamarine4","grey40")

conperc<- datafullASVs %>% filter(Project=="Controls") %>% 
   left_join(read_qza(taxonomyfile)$data, by=c("ASV"="Feature.ID")) %>% 
   unite("ASVname", c("ASV", "Taxon"), sep=": \n") %>% 
   mutate(TaxaOther=forcats::fct_lump_n(f=ASVname, w=value, other_level="Others", n=20)) %>% 
   mutate(TaxaOther=reorder(TaxaOther, -value)) %>%
   group_by(SampleName) %>% mutate(percent=value/sum(value)) %>%  
   ggplot(aes(x=SampleName, y=percent, fill=str_wrap(TaxaOther, 90)))+
   geom_col(position="fill", alpha=0.8)+
   theme(legend.text = element_text(size=12, colour="gray20", margin = margin(b = 10, unit = "pt")),
         legend.position = "right",legend.direction = "vertical",
         axis.ticks.y = element_line(inherit.blank=FALSE, color="grey30"))+
   scale_fill_manual(values=c(palettess2))+
   scale_y_continuous(labels = scales::percent_format(), expand=c(0,0))+
   labs(y="Percent ASV abundance",x=NULL,fill="ASV")


mocklegend <- get_legend(mockplot+
   guides(fill = guide_legend(ncol = 1)))
controlpercentlegend <- get_legend(conperc+
   guides(fill = guide_legend(ncol = 1)))

plot_spacer()+conreads+mockplot+conperc+
   mocklegend + controlpercentlegend + plot_layout(guides="collect", widths=c(1,5), heights=c(2,2,5)) +
   plot_annotation(tag_levels = list(c("A","B","C","",""))) &
   theme(legend.position='none', plot.tag = element_text(face = "bold", size=20))

ggsave("../figures/FigureS2_LoperamideQC_mocknegativecontrols.png", width=12, height=20)
ggsave("../figures/FigureS2_LoperamideQC_mocknegativecontrols.pdf", width=12, height=20)
ggsave("../figures/FigureS2_LoperamideQC_mocknegativecontrols.tiff", width=12, height=20)

Overall barplots

Phylum level

palettep<-c("skyblue","#D95F02", "#1B9E77", "#E6AB02","#7570B3", "#66A61E","navy","maroon","gray30")

datafullphylum %>% filter(Project=="Loperamide") %>% 
   mutate(TaxaOther=forcats::fct_lump_n(f=name, w=value, other_level="Others", n=8)) %>% 
   mutate(TaxaOther=reorder(TaxaOther, -value)) %>%
   mutate(Loperamide=recode(Loperamide, "Control water"= "Control")) %>% 
   ggplot(aes(x=SampleName, y=value, fill=TaxaOther))+
   facet_nested(.~TimepointDay+Loperamide, scales="free", space="free")+
   geom_col(position="fill", alpha=0.8)+
   theme(legend.text = element_text(size=12, colour="gray20"),
         strip.background = element_rect(color="white", fill="grey85"),
         legend.position = "bottom",axis.text.x = element_blank(), 
         axis.ticks.y = element_line(inherit.blank=FALSE, color="grey30"))+
   scale_fill_manual(values=c(palettep))+
   scale_y_continuous(labels = scales::percent_format(), expand=c(0,0))+
   labs(y="Percent phyla abundance",x=NULL,fill="Phylum")+
   guides(fill = guide_legend(ncol = 3))

ggsave("../figures/FigureS3_LoperamideBarsPhylum.png", width=12, height=6, dpi=400)
ggsave("../figures/FigureS3_LoperamideBarsPhylum.pdf", width=12, height=6)
ggsave("../figures/FigureS3_LoperamideBarsPhylum.tiff", width=12, height=6)

datafullphylum  %>% 
   group_by(SampleName) %>% mutate(percent = value/sum(value)) %>% 
   group_by(name) %>% summarise(meanPhyla = mean(percent, na.rm=TRUE), sdPhyla = sd(percent, na.rm=TRUE)) %>% 
   arrange(-meanPhyla) %>% filter(meanPhyla > 0.001) %>%  gt() %>%
  fmt_percent(
    columns = c(meanPhyla, sdPhyla),
    decimals = 1
  )

name	meanPhyla	sdPhyla
d__Bacteria; Proteobacteria	75.0%	17.4%
d__Bacteria; Bacteroidota	9.6%	9.2%
d__Bacteria; Firmicutes	5.0%	13.0%
d__Bacteria; Actinobacteriota	3.4%	2.9%
d__Bacteria; NA	3.0%	2.1%
Unassigned; NA	2.3%	1.6%
d__Bacteria; Verrucomicrobiota	0.4%	0.5%
d__Bacteria; Bdellovibrionota	0.3%	0.4%
d__Bacteria; Planctomycetota	0.2%	0.2%
d__Bacteria; Patescibacteria	0.1%	0.2%
d__Bacteria; Acidobacteriota	0.1%	0.1%
d__Bacteria; Myxococcota	0.1%	0.1%

# number of phyla and reads per phyla
datafullphylum %>% filter(Project=="Loperamide")  %>% group_by(name) %>% 
   filter(value>0) %>% 
   summarise(sumPhyla = sum(value, na.rm=TRUE),
             meanPhyla = mean(value, na.rm=TRUE))

## # A tibble: 38 × 3
##    name                          sumPhyla meanPhyla
##    <chr>                            <dbl>     <dbl>
##  1 d__Bacteria; Acidobacteriota      2862     65.0 
##  2 d__Bacteria; Actinobacteriota    64839   1441.  
##  3 d__Bacteria; Aquificota              2      2   
##  4 d__Bacteria; Armatimonadota         37      4.11
##  5 d__Bacteria; Bacteroidota       209990   4666.  
##  6 d__Bacteria; Bdellovibrionota     6484    154.  
##  7 d__Bacteria; Campilobacterota      249      8.59
##  8 d__Bacteria; Chloroflexi           675     17.8 
##  9 d__Bacteria; Cloacimonadota          8      8   
## 10 d__Bacteria; Deferribacterota       13      4.33
## # … with 28 more rows

Genus level

palettess2<-c("#A6CEE3", "#1F78B4", "#B2DF8A", "#33A02C", "#FB9A99", "#E31A1C", "#FDBF6F", "#FF7F00", 
              "#CAB2D6", "#6A3D9A", "#FFFF99", "#B15928","blue1","navy",
              "maroon2","maroon4","burlywood1","burlywood4","aquamarine3","aquamarine4","grey40")

genusbarplot <- datafullgenus %>% filter(Project=="Loperamide") %>% 
   mutate(name=recode(name, "Unassigned; NA; NA; NA; NA; NA"="NA; NA; NA; NA; NA")) %>% 
   mutate(TaxaOther=forcats::fct_lump_n(f=name, w=value, other_level="Others", n=13)) %>% 
   mutate(TaxaOther=str_remove(TaxaOther, ".*d__Bacteria; ")) %>% 
   mutate(TaxaOther=recode(TaxaOther, "NA; NA; NA; NA; NA"= "Unknown bacteria")) %>% 
   mutate(TaxaOther=reorder(TaxaOther, -value)) %>%
   mutate(Loperamide=recode(Loperamide, "Control water"= "Control")) %>% 
   ggplot(aes(x=SampleName, y=value, fill=TaxaOther))+
   facet_nested(.~TimepointDay+Loperamide, scales="free", space="free")+
   geom_col(position="fill", alpha=0.8)+
   theme(legend.text = element_text(size=14, colour="gray20"),
         legend.position = "bottom", legend.direction="vertical", 
         axis.text.x = element_blank(),
         strip.background = element_rect(color="white", fill="grey85"),
         axis.ticks.y = element_line(inherit.blank=FALSE, color="grey30"))+
   scale_fill_manual(values=c(palettess))+
   scale_y_continuous(labels = scales::percent_format(), expand=c(0,0))+
   labs(y="Percent genus abundance",x=NULL,fill="Genus")+ 
   guides(fill = guide_legend(ncol = 1))

genusbarplot

Venn Diagram

Genus level

library(UpSetR)
library(gplots)
library(ComplexHeatmap)

DMSO24hsetG <- datafullgenus %>% filter(LoperamideTimepoint=="DMSO.T0") %>% 
   filter(value >5) %>% distinct(name) %>% .$name
DMSO48hsetG <- datafullgenus %>% filter(LoperamideTimepoint=="DMSO.T1") %>% 
   filter(value >5) %>% distinct(name) %>% .$name
DMSO6dsetG <- datafullgenus %>% filter(LoperamideTimepoint=="DMSO.T5") %>% 
   filter(value >5) %>% distinct(name) %>% .$name
Loperamide24hsetG <- datafullgenus %>% filter(LoperamideTimepoint=="Loperamide.T0") %>% 
   filter(value >5) %>% distinct(name) %>% .$name
Loperamide48hsetG <- datafullgenus %>% filter(LoperamideTimepoint=="Loperamide.T1") %>% 
   filter(value >5) %>% distinct(name) %>% .$name
Loperamide6dsetG <- datafullgenus %>% filter(LoperamideTimepoint=="Loperamide.T5") %>% 
   filter(value >5) %>% distinct(name) %>% .$name

read_setsG = list("DMSO T0" = DMSO24hsetG, "DMSO T1" = DMSO48hsetG, "DMSO T5" = DMSO6dsetG, 
                 "Loperamide T0" = Loperamide24hsetG, "Loperamide T1" = Loperamide48hsetG,
                 "Loperamide T5" = Loperamide6dsetG)
mG = make_comb_mat(read_setsG)

(upsetGplot <- UpSet(mG,      
      set_order = order(c("DMSO.T0","DMSO.T1","DMSO.T5",
                          "Loperamide.T0","Loperamide.T1","Loperamide.T5")),
      pt_size = unit(.5, "cm"),lwd=2.5,
      comb_col = c("black","grey50","grey50","grey50","grey50","grey50","grey50","grey50","grey50","grey50","grey50","grey50",
                   "grey50","grey50","grey50","grey50","grey50","grey50","grey50","grey50","grey50","grey50","grey50","grey50",
                   "grey50","grey50","grey50","grey50","grey50","grey50","grey50","grey50","grey50","grey50","grey50","grey50","maroon",
                   "grey50","grey50","grey50","grey50","grey50","grey50","grey50","grey50","grey50","grey50","grey50",
                   "maroon","grey50","grey50",
                   '#5a89b8',  '#1c5580', '#00264c', '#69fcc6','#0fc08e', '#008759'),
      right_annotation = rowAnnotation(" " = anno_barplot(set_size(mG), bar_width=0.7,
                                                         axis_param = list(side = "top",labels_rot = 0),
                                                         border = FALSE, annotation_name_side = "top", 
                                                         gp = gpar(fill = c('#5a89b8',  '#1c5580', '#00264c', '#69fcc6','#0fc08e', '#008759')),
                                                         width = unit(4, "cm"))),
      row_names_side = "left",
      top_annotation = upset_top_annotation(mG,bar_width = 0.9, height = unit(6, "cm"))))

Summary figure with barplots

genusbarplot / ggplotify::as.grob(upsetGplot)+plot_annotation(tag_levels = "A")+plot_layout(heights=c(3,2.5)) &
   theme(plot.tag = element_text(face = "bold", size=20))

ggsave("../figures/FigureS4_LoperamideBarsGenusUpset.png", width=12.5, height=14, dpi=400)
ggsave("../figures/FigureS4_LoperamideBarsGenusUpset.pdf", width=12.5, height=14)
ggsave("../figures/FigureS4_LoperamideBarsGenusUpset.tiff", width=12.5, height=14)

Limma Differential Abundance

At genus level - calculate differentiall abundant taxa.

mm_limma_24h <- run_limma_voom(
    Loperamidephyseq, norm="RLE",
    "LoperamideTimepoint",
    pvalue_cutoff = 0.05,
    taxa_rank = "Genus",
    contrast = c("DMSO.24h", "Loperamide.24h"),
    p_adjust = "fdr"
)

mm_limma_48h <- run_limma_voom(
    Loperamidephyseq,  norm="RLE",
    "LoperamideTimepoint",
    pvalue_cutoff = 0.05,
    taxa_rank = "Genus",
    contrast = c("DMSO.48h", "Loperamide.48h"),
    p_adjust = "fdr"
)

mm_limma_6d <- run_limma_voom(
    Loperamidephyseq,  norm="RLE",
    "LoperamideTimepoint",
    pvalue_cutoff = 0.05,
    taxa_rank = "Genus",
    contrast = c("DMSO.6d", "Loperamide.6d"),
    p_adjust = "fdr"
)

limmares24h <- marker_table(mm_limma_24h) %>% data.frame() %>% add_column(comparison="T0")
limmares48h <- marker_table(mm_limma_48h) %>% data.frame()%>% add_column(comparison="T1")
limmares6d <- marker_table(mm_limma_6d) %>% data.frame() %>% add_column(comparison="T5")

alllimmaresults <- full_join(limmares24h, limmares48h)  %>% full_join(limmares6d)

Plot with abundant highlighed

abundGenusTopOnePercent <- datafullgenus %>% filter(Project=="Loperamide") %>% #filter(Loperamide!="Control water") %>% 
   filter(Loperamide %in% c("DMSO","Loperamide")) %>% 
   group_by(SampleName) %>% mutate(percent=(value/sum(value))) %>% 
   group_by(name) %>% mutate(meanperc=mean(percent)) %>% 
   filter(meanperc>0.01) %>% 
   mutate(GenusClean = str_replace_all(name, "; NA", "_g__"),
          GenusClean = sub(".*; ", "", GenusClean)) %>% 
   mutate(GenusClean = recode(GenusClean, "Gammaproteobacteria_g___g___g__"="Gammaproteobacteria_unknown",
                              "Unassigned_g___g___g___g___g__"="Unknown",
                              "d__Bacteria_g___g___g___g___g__"="Unknown",))

limmagenus<- alllimmaresults %>% 
   mutate(enrich_group = factor(enrich_group, 
                          levels = c("DMSO.24h", "DMSO.48h","DMSO.6d",
                                     "Loperamide.24h","Loperamide.48h","Loperamide.6d"),
                          labels = c("DMSO.T0", "DMSO.T1","DMSO.T5",
                                     "Loperamide.T0","Loperamide.T1","Loperamide.T5"))) %>% 
   mutate(feature = recode(feature, "Actinobacteriota_c___o___f___g__" = "Actinobacteriota")) %>% 
   mutate(highlighttaxa = case_when(feature %in% abundGenusTopOnePercent$GenusClean ~ ">1% abundance",
                                    TRUE ~ "low abundance")) %>% 
   mutate(featurebold = case_when(feature %in% abundGenusTopOnePercent$GenusClean ~ 
                                     paste0("<span style='color:black'>**",feature,"**</span>"),
                                    TRUE ~ feature)) %>% 
   filter(abs(ef_logFC) > 2) %>% 
   ggplot(aes(x=ef_logFC, y=reorder(featurebold,ef_logFC), fill=enrich_group))+
   geom_col(position="dodge", aes(color=highlighttaxa), lwd=1.5, width=0.8)+
   facet_grid(comparison~., scales="free", space="free")+
   scale_fill_manual(values=c('#5a89b8',  '#1c5580', '#00264c', '#69fcc6','#0fc08e', '#008759'))+
   scale_color_manual(values=c("black", "transparent"), guide = "none") +
   theme(legend.position="bottom",legend.direction = "vertical",
         axis.text.y=element_markdown(color="grey40"),
         panel.grid.major.x = element_line(color="gray70"),panel.grid.major.y = element_line(size=0.2))+
   labs(fill="Enriched \ngroup", x="Log fold change", y=NULL)+ 
   guides(fill = guide_legend(ncol = 2,title.position = "left", hjust = 1))

limmagenus

Alpha Diversity - Simpsons

See ZebrafishMixCFUs_loperamide.Rmd script to generate figure 6 with mix diversity.

# calculate diversity
diversitysimpsons<-diversity(matrixLoperamide, index="simpson")
# add into metadata variable
metadataLoperamide$Simpsons<-diversitysimpsons

# calculate stats
statsSimpsons <- compare_means(data=metadataLoperamide, 
                            Simpsons~Loperamide, 
                            group.by = c("TimepointDay")) %>% 
   filter(p.format<0.05 & group1=="DMSO")

convalpha <- metadataLoperamide %>% filter(Project=="Loperamide") %>% 
   mutate(Loperamide=factor(Loperamide, levels=c("Control water", "DMSO", "Loperamide"),
                            labels=c("Control", "DMSO", "Loperamide"))) %>% 
   ggplot(aes(x=TimepointDay, y=Simpsons, 
             fill=Loperamide, color=Loperamide, shape=Loperamide))+
   geom_boxplot(color="black", alpha=0.8, show.legend = FALSE)+
   geom_point(size=2, position=position_jitterdodge(jitter.width=0.25)) +
   geom_text(data=statsSimpsons, aes(label="*", x=TimepointDay, 
                                        y=0.9, fill=NA, shape=NA), 
             size=9, color="#0fc08e", show.legend=FALSE, nudge_x=.3)+
   scale_color_manual(values=c('#000000', '#1c5580', '#0fc08e'))+
   scale_fill_manual(values=c('#000000', '#1c5580', '#0fc08e'))+
   scale_y_continuous(limits=c(0,1.0), expand=c(0,0))+
   theme(legend.position = "none")+
   labs(x=NULL, y="Simpson's Index of Diversity",fill=NULL, shape=NULL, color=NULL,
        title="Conventional fish")+
   theme(legend.position = "bottom", plot.title = element_text(size=20),
         panel.background = element_rect(color="grey80", size=0.8))+
   guides(color=guide_legend(override.aes = list(size=4)))

convalpha

Beta Diversity

# ellipse function
veganCovEllipse<-function (cov, center = c(0, 0), scale = 1, npoints = 100)
{
   theta <- (0:npoints) * 2 * pi/npoints
   Circle <- cbind(cos(theta), sin(theta))
   t(center + scale * t(Circle %*% chol(cov)))
}

All conditions

ggplot(data=NMDS,aes(x,y,colour=LoperamideTimepoint,fill=LoperamideTimepoint))+theme_bw() +
  geom_path(data=df_ell, aes(x=NMDS1, y=NMDS2, lty=LoperamideTimepoint), size=1) +
  geom_point(size=4, alpha=0.9,aes(shape=TimepointDay))+scale_shape_manual(values = c(21,22,23,24,25))+
  annotate("text",x=NMDS.mean$x,y=NMDS.mean$y,label=NMDS.mean$group,size=5, color="gray40") +
  scale_color_manual(values=c("grey80","grey50","grey20",'#5a89b8',  '#1c5580', '#00264c', '#69fcc6','#0fc08e', '#008759')) +
  scale_fill_manual(values=c("grey80","grey50","grey20",'#5a89b8',  '#1c5580', '#00264c', '#69fcc6','#0fc08e', '#008759')) +
  scale_linetype_manual(values=c("solid","solid","solid","dotted","dotted","dotted","dashed","dashed","dashed"))+
  theme(legend.text = element_text(size=14, colour="gray20"), legend.position = "right",
        legend.title = element_blank(),legend.box="horizontal")

df_ell <- df_ell %>% left_join(distinct(metadataLoperamide, 
                                        LoperamideTimepoint,TimepointDay, Loperamide))
NMDS <- NMDS %>% left_join(distinct(metadataLoperamide, 
                                        LoperamideTimepoint,TimepointDay, Loperamide))

allbetaplot <- ggplot(data=NMDS,aes(x,y))+
  geom_path(data=df_ell, aes(x=NMDS1, y=NMDS2, colour=Loperamide, lty=TimepointDay), size=1) +
  geom_point(aes(color=Loperamide, shape=TimepointDay),size=4, alpha=0.9)+
  scale_shape_manual(values=c(15,16,17))+
  annotate("text",x=NMDS.mean$x,y=NMDS.mean$y,label=NMDS.mean$group,size=5,
           color=c("#000000","#000000","#000000",'#1c5580','#1c5580',
                   '#1c5580','#0fc08e','#0fc08e','#0fc08e')) +
  annotate("richtext", label=paste0("*k* = 2<br>stress = ", round(sol$stress,3)), 
            hjust=0, x=-1.8, y=-1.3, color="grey20", label.size = NA, size = 6, fill=NA) + 
  scale_color_manual(values=c("#000000",'#1c5580','#0fc08e')) +
  scale_linetype_manual(values=c("solid","dotted","dashed","solid","dotted","dashed","solid","dotted","dashed"))+
  theme(legend.text = element_text(size=14, colour="gray20"), 
        legend.position = "bottom", legend.direction="vertical",
        legend.title = element_blank(),legend.box="horizontal",
        legend.key.width = unit(3,"cm"))+
   guides(color=guide_legend(override.aes = list(size=5, shape=15)))
allbetaplot

Just DMSO and loperamide

betadivplot<- ggplot(data=NMDS,aes(x,y,colour=LoperamideTimepoint,fill=LoperamideTimepoint))+
   geom_path(data=df_ell, aes(x=NMDS1, y=NMDS2, lty=LoperamideTimepoint), size=0.8) +
   geom_point(size=5, alpha=0.7, aes(shape=LoperamideTimepoint), color="grey30")+
   scale_shape_manual(values=c(24,24,24,22,22,22))+
   annotate("label",x=NMDS.mean$x,y=NMDS.mean$y,label=NMDS.mean$group,size=5,
           color="black", label.size=NA,
           fill=alpha(c('#5a89b8',  '#1c5580', '#00264c', '#69fcc6','#0fc08e', '#008759'),0.4)) +
   annotate("richtext", label=paste0("stress = ", round(sol$stress,3),"<br>*k* = 2"), 
            hjust=0, x=1, y=-1, color="grey20", label.size = NA, size = 6, fill=NA) + 
   scale_color_manual(values=c('#5a89b8',  '#1c5580', '#00264c', '#69fcc6','#0fc08e', '#008759')) +
   scale_fill_manual(values=c('#5a89b8',  '#1c5580', '#00264c', '#69fcc6','#0fc08e', '#008759')) +
   scale_linetype_manual(values=c("solid","dotted","dashed","solid","dotted","dashed"))+
   theme(legend.text = element_text(size=18), legend.position="bottom",
        legend.title = element_blank(),legend.direction="vertical", legend.key.width = unit(2,"cm"))+ 
   guides(shape = guide_legend(ncol = 2))
betadivplot

Summary figure with limma results

betadivplot+limmagenus+
   plot_annotation(tag_levels = "A",
                   theme = theme(plot.title = element_text(size = 20)))+
   plot_layout(widths=c(4,3))&
   theme(plot.tag = element_text(face = "bold", size=20))

ggsave("../figures/Figure2_LoperamideBetaAll_LimmaGenus.png", bg="transparent", width = 15, height = 10)
ggsave("../figures/Figure2_LoperamideBetaAll_LimmaGenus.pdf", bg="transparent", width = 15, height = 10)
ggsave("../figures/Figure2_LoperamideBetaAll_LimmaGenus.tiff", bg="transparent", width = 15, height = 10)

Each timepoint separately

library(patchwork)

betaindiv<- loperamide1 +loperamide2 + loperamide3 + plot_layout(guides = "collect") & 
   theme(legend.position = "right",legend.key.width = unit(2,"cm"),
         plot.title=element_text(size=24),
         plot.background = element_blank(), legend.background = element_blank())

Within group beta-diversity

braycurtisdistances<-
  # calculate the dissimilarity matrix between each sample
  vegdist(matrixLoperamidePercent, method="bray", k=2) %>% as.matrix() %>% as.data.frame() %>% 
  # Add in the sites based on the rows (paired sample)
  rownames_to_column(var="PairedID")  %>% mutate(PairedCondition=metadataLoperamide$LoperamideTimepoint) %>% 
  # Make into longform based on the columns (OG SampleID)
  pivot_longer(cols="1-Conv-1-1":"45-LOP-3-5", names_to="SampleID") %>% 
  # Add in sites based on the columns (OG SampleID)
  left_join(metadataLoperamide) %>% 
  # Remove rows where a sample is paired with a sample from another condition
  filter(PairedCondition==LoperamideTimepoint) %>% 
  # Remove the diagonal rows, where each sample was compared to itself
  filter(value!=0)

# stats
compare_means(data=braycurtisdistances, value~LoperamideTimepoint, method="wilcox", p.adjust.method = "BH")

## # A tibble: 36 × 8
##    .y.   group1          group2                p  p.adj p.format p.signif method
##    <chr> <chr>           <chr>             <dbl>  <dbl> <chr>    <chr>    <chr> 
##  1 value ControlWater.T0 ControlWater.T1 2.54e-3 4.6e-3 0.00254  **       Wilco…
##  2 value ControlWater.T0 ControlWater.T5 1.57e-5 4.3e-5 1.6e-05  ****     Wilco…
##  3 value ControlWater.T0 DMSO.T0         7.56e-1 8.5e-1 0.75552  ns       Wilco…
##  4 value ControlWater.T0 DMSO.T1         2.85e-1 3.5e-1 0.28485  ns       Wilco…
##  5 value ControlWater.T0 DMSO.T5         8.39e-1 8.9e-1 0.83908  ns       Wilco…
##  6 value ControlWater.T0 Loperamide.T0   5.56e-4 1.1e-3 0.00056  ***      Wilco…
##  7 value ControlWater.T0 Loperamide.T1   1.32e-2 2.1e-2 0.01323  *        Wilco…
##  8 value ControlWater.T0 Loperamide.T5   4.57e-1 5.3e-1 0.45653  ns       Wilco…
##  9 value ControlWater.T1 ControlWater.T5 3.70e-4 7.8e-4 0.00037  ***      Wilco…
## 10 value ControlWater.T1 DMSO.T0         5.76e-6 1.9e-5 5.8e-06  ****     Wilco…
## # … with 26 more rows

compare_means(data=braycurtisdistances, value~LoperamideTimepoint, method="kruskal")

## # A tibble: 1 × 6
##   .y.          p   p.adj p.format p.signif method        
##   <chr>    <dbl>   <dbl> <chr>    <chr>    <chr>         
## 1 value 9.90e-18 9.9e-18 <2e-16   ****     Kruskal-Wallis

# plot within condition variation
withindiss <- ggplot(braycurtisdistances, aes(x=Loperamide, y=value, fill=Loperamide, shape=Loperamide))+
   geom_jitter(width=0.15, size=2, alpha=0.7)+geom_boxplot(alpha=0.8)+
   labs(x=NULL,y="within condition dissimilarity index",fill="Condition")+
   facet_grid(.~TimepointDay, space="free", scales="free")+
   stat_compare_means(ref.group="DMSO", label="p.signif")+
   scale_fill_manual(values=c('#000000','#1c5580', '#0fc08e'))+
   scale_shape_manual(values=c(21,24,22))+
   scale_y_continuous(limits=c(0,0.8), labels=c("0.00","0.25","0.50","0.75","1.00"))+
   theme(legend.position = "none", strip.background = element_rect("grey85", color="transparent"))

Summary plot Beta diversity

(allbetaplot  & theme(legend.position=c(0.25,0.86))) /
   (betaindiv  & theme(legend.position="bottom"))/
   (withindiss & theme()) +plot_annotation(tag_levels = c('A','B'))+
   plot_layout(heights=c(3.5,2,2.5)) &
   theme(plot.tag = element_text(face = "bold", size=20))

ggsave("../figures/FigureS5_16Sloperamide_BetaDaysAndWithinDiv.png", bg="transparent", width=11, height=16)
ggsave("../figures/FigureS5_16Sloperamide_BetaDaysAndWithinDiv.pdf", bg="transparent", width=11, height=16)
ggsave("../figures/FigureS5_16Sloperamide_BetaDaysAndWithinDiv.tiff", bg="transparent", width=11, height=16)

Session Info

sessionInfo()

## R version 4.1.3 (2022-03-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur/Monterey 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] ComplexHeatmap_2.10.0  gplots_3.1.1           UpSetR_1.4.0          
##  [4] ggtext_0.1.1           ggh4x_0.2.1            patchwork_1.1.1       
##  [7] microbiomeMarker_1.3.2 gt_0.4.0               vegan_2.5-7           
## [10] lattice_0.20-45        permute_0.9-7          qiime2R_0.99.6        
## [13] ggpubr_0.4.0           scales_1.1.1           forcats_0.5.1         
## [16] stringr_1.4.0          dplyr_1.0.8            purrr_0.3.4           
## [19] readr_2.1.2            tidyr_1.2.0            tibble_3.1.6          
## [22] ggplot2_3.3.5          tidyverse_1.3.1       
## 
## loaded via a namespace (and not attached):
##   [1] utf8_1.2.2                  tidyselect_1.1.2           
##   [3] RSQLite_2.2.11              AnnotationDbi_1.56.2       
##   [5] htmlwidgets_1.5.4           BiocParallel_1.28.3        
##   [7] Rtsne_0.15                  munsell_0.5.0              
##   [9] codetools_0.2-18            DT_0.22                    
##  [11] withr_2.5.0                 colorspace_2.0-3           
##  [13] Biobase_2.54.0              phyloseq_1.38.0            
##  [15] highr_0.9                   knitr_1.38                 
##  [17] rstudioapi_0.13             stats4_4.1.3               
##  [19] ggsignif_0.6.3              labeling_0.4.2             
##  [21] MatrixGenerics_1.6.0        Rdpack_2.3                 
##  [23] GenomeInfoDbData_1.2.7      plotROC_2.2.1              
##  [25] farver_2.1.0                bit64_4.0.5                
##  [27] rhdf5_2.38.1                treeio_1.18.1              
##  [29] vctrs_0.4.0                 generics_0.1.2             
##  [31] xfun_0.30                   markdown_1.1               
##  [33] R6_2.5.1                    doParallel_1.0.17          
##  [35] GenomeInfoDb_1.30.1         clue_0.3-60                
##  [37] locfit_1.5-9.5              bitops_1.0-7               
##  [39] rhdf5filters_1.6.0          microbiome_1.16.0          
##  [41] cachem_1.0.6                gridGraphics_0.5-1         
##  [43] DelayedArray_0.20.0         assertthat_0.2.1           
##  [45] nnet_7.3-17                 gtable_0.3.0               
##  [47] rlang_1.0.2                 genefilter_1.76.0          
##  [49] GlobalOptions_0.1.2         splines_4.1.3              
##  [51] rstatix_0.7.0               lazyeval_0.2.2             
##  [53] broom_0.7.12                checkmate_2.0.0            
##  [55] yaml_2.3.5                  reshape2_1.4.4             
##  [57] abind_1.4-5                 modelr_0.1.8               
##  [59] backports_1.4.1             Hmisc_4.6-0                
##  [61] gridtext_0.1.4              tools_4.1.3                
##  [63] zCompositions_1.4.0-1       ggplotify_0.1.0            
##  [65] ellipsis_0.3.2              jquerylib_0.1.4            
##  [67] biomformat_1.22.0           RColorBrewer_1.1-2         
##  [69] BiocGenerics_0.40.0         Rcpp_1.0.8.3               
##  [71] plyr_1.8.7                  base64enc_0.1-3            
##  [73] zlibbioc_1.40.0             RCurl_1.98-1.6             
##  [75] Wrench_1.12.0               rpart_4.1.16               
##  [77] GetoptLong_1.0.5            S4Vectors_0.32.4           
##  [79] SummarizedExperiment_1.24.0 haven_2.4.3                
##  [81] cluster_2.1.3               fs_1.5.2                   
##  [83] magrittr_2.0.3              data.table_1.14.2          
##  [85] circlize_0.4.14             reprex_2.0.1               
##  [87] truncnorm_1.0-8             matrixStats_0.61.0         
##  [89] hms_1.1.1                   evaluate_0.15              
##  [91] xtable_1.8-4                XML_3.99-0.9               
##  [93] jpeg_0.1-9                  readxl_1.4.0               
##  [95] IRanges_2.28.0              gridExtra_2.3              
##  [97] shape_1.4.6                 compiler_4.1.3             
##  [99] KernSmooth_2.23-20          crayon_1.5.1               
## [101] htmltools_0.5.2             ggfun_0.0.5                
## [103] mgcv_1.8-39                 tzdb_0.3.0                 
## [105] Formula_1.2-4               geneplotter_1.72.0         
## [107] aplot_0.1.2                 ANCOMBC_1.4.0              
## [109] lubridate_1.8.0             DBI_1.1.2                  
## [111] dbplyr_2.1.1                MASS_7.3-56                
## [113] Matrix_1.4-1                ade4_1.7-18                
## [115] car_3.0-12                  cli_3.2.0                  
## [117] rbibutils_2.2.7             parallel_4.1.3             
## [119] igraph_1.2.11               GenomicRanges_1.46.1       
## [121] pkgconfig_2.0.3             foreign_0.8-82             
## [123] xml2_1.3.3                  foreach_1.5.2              
## [125] ggtree_3.2.1                annotate_1.72.0            
## [127] bslib_0.3.1                 multtest_2.50.0            
## [129] XVector_0.34.0              rvest_1.0.2                
## [131] NADA_1.6-1.1                yulab.utils_0.0.4          
## [133] digest_0.6.29               Biostrings_2.62.0          
## [135] rmarkdown_2.13              cellranger_1.1.0           
## [137] tidytree_0.3.9              htmlTable_2.4.0            
## [139] gtools_3.9.2                rjson_0.2.21               
## [141] nloptr_2.0.0                lifecycle_1.0.1            
## [143] nlme_3.1-157                jsonlite_1.8.0             
## [145] Rhdf5lib_1.16.0             carData_3.0-5              
## [147] limma_3.50.1                fansi_1.0.3                
## [149] pillar_1.7.0                KEGGREST_1.34.0            
## [151] fastmap_1.1.0               httr_1.4.2                 
## [153] survival_3.3-1              glue_1.6.2                 
## [155] png_0.1-7                   iterators_1.0.14           
## [157] glmnet_4.1-3                bit_4.0.4                  
## [159] metagenomeSeq_1.36.0        stringi_1.7.6              
## [161] sass_0.4.1                  blob_1.2.2                 
## [163] DESeq2_1.34.0               caTools_1.18.2             
## [165] latticeExtra_0.6-29         memoise_2.0.1              
## [167] ape_5.6-2

Loperamide 16S V6 amplicons analysis

Rebecca Stevick

About the Data

Conditions

Sampling

Sequencing

Analysis

Setup

Load libraries and settings

Import data

Clean up data

Make data frames per rank

Other formats

General QC

Reads per sample

Rarefaction curves

Controls

Overall barplots

Phylum level

Genus level

Venn Diagram

Genus level

Summary figure with barplots

Limma Differential Abundance

Plot with abundant highlighed

Alpha Diversity - Simpsons

Beta Diversity

All conditions

Just DMSO and loperamide

Summary figure with limma results

Each timepoint separately

Within group beta-diversity

Summary plot Beta diversity

Session Info